6_probabilistic_ra
LTC Stephen Lewandowski, PhD, CPH
Department of Preventive Medicine and Biostatistics (OEHS)
11/10/22
Lesson Objectives
- Explain rational for the use of probabilistic risk assessment
- Describe the tiered approach for risk assessment
- Understand the basics of Monte Carlo sampling
- Discuss the advantages and disadvantages of using probabilistic assessments compared to deterministic assessments
Probabilistic Risk Assessment
- “An analytical methodology used to incorporate information regarding uncertainty and/or variability into analyses to provide insight regarding the degree of certainty of a risk estimate and how the risk estimate varies among different members of an exposed population…”
A group of techniques that incorporate uncertainty and variability into risk assessments
EPA, 2014 Risk Assessment Forum White Paper
Variability and Uncertainty Review
Variability: the inherent natural variation, diversity and heterogeneity across time, space or individuals within a population or lifestage
Uncertainty: imperfect knowledge or a lack of precise knowledge of the physical world, either for specific values of interest or in the description of the system
Probabilistic Approach - Motivation
Risk assessors, risk managers and others, particularly within the scientific and research divisions, have recognized that more
sophisticated statistical and mathematical approaches could be utilized to enhance the quality and accuracy of Agency risk assessments
Various stakeholders, inside and outside of the Agency, have called for a more comprehensive characterization of risks, including uncertainties, to improve the protection of sensitive or vulnerable populations and lifestages
EPA, 2014 Risk Assessment Forum White Paper
Deterministic: Point Estimate of Exposure Dose
- Deterministic risk assessments express health risks as a single numerical estimate of risk
- Assuming reasonable maximum exposure
- Compounds unrealistically high estimates
- Difficult to know/communicate the level of conservatism
- Assuming average exposure
- May present unacceptable risks
- Mostly qualitative assessment of uncertainty and variability
Tiered Approach for Risk Assessment
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Assessments that are high in complexity and regulatory significance benefit from the application of probabilistic techniques
Probabilistic Approach
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Probabilistic Example Scenario: AIRBORNE!
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Probabilistic Example: Nuclear Power Plant Operations (NRC)
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Monte Carlo Simulation
Mathematical technique used to estimate the possible outcomes of an uncertain event
Predicts a set of outcomes based on an estimated range of values versus a set of fixed input values Yields a range of possible outcomes with the probability of each result occurring
Sensitivity analysis
Monte Carlo Simulation Applications
- Intergovernmental Panel on Climate Change: probability density function analysis of radiative forcing
- Computer graphics: Path/ray tracing renders a 3D scene by randomly tracing samples of possible light paths
- US Coast Guard: computer modeling software SAROPS calculates the probable locations of vessels during search and rescue operations
Monte Carlo Simulation: IPCC (WG1AR5)
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Monte Carlo Simulation: IPCC (WG1AR5)
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Monte Carlo Simulation: IPCC (WG1AR5)
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Monte Carlo Simulation: SAROPS
Search and Rescue Optimal Planning System (SAROPS)
Software used by the U.S. Coast Guard for Maritime Search Planning
SAROPS is a Monte Carlo based system that uses thousands of simulated particles generated by user inputs in a wizard based Graphical User Interface
- Handle multiple scenarios and search object types
- Model pre-distress motion and hazards
- Account for the affects of previous searches
Monte Carlo Simulation: SAROPS Screen
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Monte Carlo Simulation: Step 1
Set up the predictive model, identifying both the dependent variable to be predicted and the independent variables (also known as the input, risk or predictor variables) that will drive the prediction.
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https://www.ibm.com/cloud/learn/monte-carlo-simulation
Monte Carlo Simulation: Step 2
Specify probability distributions of the independent variables.
Use historical data and/or the analyst’s subjective judgment to define a range of likely values and assign probability weights for each.
Probability distribution: mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment
- Type of distribution is useful when you need to know which outcomes are most likely, the spread of potential values, and the likelihood of different results
- Selection of the appropriate distribution depends on the presence or absence of symmetry of the data set with respect to the mean value
https://www.ibm.com/cloud/learn/monte-carlo-simulation
Monte Carlo Simulation: Step 2 (Distributions)
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Monte Carlo Simulation: Step 3
Run simulations repeatedly, generating random values of the independent variables
Do this until enough results are gathered to make up a representative sample of the near infinite number of possible combinations
Advantages and Disadvantages of Probabilistic Assessment
- Advantages
- Flexibility for risk managers
- Quantifies uncertainty and variability
- Range of risk opposed to a single point estimate
- Disadvantages may be offset by more informed decisions
- Disadvantages
- Time
- Resources
- Greater technical expertise (analysts, communicators, and decision makers)
- May require more data
- Communicating results
Simulation Programs
Commercial Software
Argo [BAH. Open Source]
@Risk [Palisade, \~\$1,900]
Crystal Ball [Oracle, ~$1,000]
Riskamp [Structured Data, LLC, ~$250]
Programming Languages
R
Python
Assessments that are high in complexity and regulatory significance benefit from the application of probabilistic techniques
https://www.researchgate.net/figure/Monte-Carlo-simulation-procedure-using-ViscoWave_fig7_311982513
https://towardsdatascience.com/an-overview-of-monte-carlo-methods-675384eb1694
Simulation Example: https://github.com/boozallen/argo/wiki/First-Argo-Simulation-Model
